
"""simple docstring"""

from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
   import datasets
class   _UpperCAmelCase				(		snake_case__				):




      @slow
      @require_torch
      def      a_   (				self				)    ->       Any:
          UpperCAmelCase          =       EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny' ,							'prajjwal1/bert-tiny'				)
          UpperCAmelCase          =       BertTokenizer.from_pretrained('bert-base-uncased'				)

          UpperCAmelCase          =       bertabert.config.encoder.vocab_size
          UpperCAmelCase          =       tokenizer.sep_token_id
          UpperCAmelCase          =       tokenizer.cls_token_id
          UpperCAmelCase          =       1_2_8

          UpperCAmelCase          =       datasets.load_dataset('cnn_dailymail' ,							'3.0.0' ,							split='train[:1%]'				)
          UpperCAmelCase          =       datasets.load_dataset('cnn_dailymail' ,							'3.0.0' ,							split='validation[:1%]'				)

          UpperCAmelCase          =       train_dataset.select(range(3_2				)				)
          UpperCAmelCase          =       val_dataset.select(range(1_6				)				)

          UpperCAmelCase          =       4

          def _map_to_encoder_decoder_inputs(lowercase_				):
              # Tokenizer will automatically set [BOS] <text> [EOS]
              UpperCAmelCase          =       tokenizer(batch['article'] ,							padding='max_length' ,							truncation=_UpperCAmelCase ,							max_length=5_1_2				)
              UpperCAmelCase          =       tokenizer(batch['highlights'] ,							padding='max_length' ,							truncation=_UpperCAmelCase ,							max_length=1_2_8				)
              UpperCAmelCase          =       inputs.input_ids
              UpperCAmelCase          =       inputs.attention_mask

              UpperCAmelCase          =       outputs.input_ids
              UpperCAmelCase          =       outputs.input_ids.copy()
              UpperCAmelCase          =       [
                  [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
              ]
              UpperCAmelCase          =       outputs.attention_mask

              assert all(len(_UpperCAmelCase				) == 5_1_2 for x in inputs.input_ids				)
              assert all(len(_UpperCAmelCase				) == 1_2_8 for x in outputs.input_ids				)

              return batch

          def _compute_metrics(lowercase_				):
              UpperCAmelCase          =       pred.label_ids
              UpperCAmelCase          =       pred.predictions

              # all unnecessary tokens are removed
              UpperCAmelCase          =       tokenizer.batch_decode(_UpperCAmelCase ,							skip_special_tokens=_UpperCAmelCase				)
              UpperCAmelCase          =       tokenizer.batch_decode(_UpperCAmelCase ,							skip_special_tokens=_UpperCAmelCase				)

              UpperCAmelCase          =       sum([int(pred_str[i] == label_str[i]				) for i in range(len(_UpperCAmelCase				)				)]				) / len(_UpperCAmelCase				)

              return {"accuracy": accuracy}

          # map train dataset
          UpperCAmelCase          =       train_dataset.map(
              _map_to_encoder_decoder_inputs ,							batched=_UpperCAmelCase ,							batch_size=_UpperCAmelCase ,							remove_columns=['article', 'highlights'] ,							)
          train_dataset.set_format(
              type='torch' ,							columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels'] ,							)

          # same for validation dataset
          UpperCAmelCase          =       val_dataset.map(
              _map_to_encoder_decoder_inputs ,							batched=_UpperCAmelCase ,							batch_size=_UpperCAmelCase ,							remove_columns=['article', 'highlights'] ,							)
          val_dataset.set_format(
              type='torch' ,							columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels'] ,							)

          UpperCAmelCase          =       self.get_auto_remove_tmp_dir()

          UpperCAmelCase          =       SeqaSeqTrainingArguments(
              output_dir=_UpperCAmelCase ,							per_device_train_batch_size=_UpperCAmelCase ,							per_device_eval_batch_size=_UpperCAmelCase ,							predict_with_generate=_UpperCAmelCase ,							evaluation_strategy='steps' ,							do_train=_UpperCAmelCase ,							do_eval=_UpperCAmelCase ,							warmup_steps=0 ,							eval_steps=2 ,							logging_steps=2 ,							)

          # instantiate trainer
          UpperCAmelCase          =       SeqaSeqTrainer(
              model=_UpperCAmelCase ,							args=_UpperCAmelCase ,							compute_metrics=_compute_metrics ,							train_dataset=_UpperCAmelCase ,							eval_dataset=_UpperCAmelCase ,							tokenizer=_UpperCAmelCase ,							)

          # start training
          trainer.train()








'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


SCREAMING_SNAKE_CASE__     :						List[str]	=  logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all BART models at https://huggingface.co/models?filter=bart
SCREAMING_SNAKE_CASE__     :						Dict	=  {
    '''vocab_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json''',
    },
}

SCREAMING_SNAKE_CASE__     :						Optional[int]	=  {
    '''facebook/bart-base''': 1_0_2_4,
    '''facebook/bart-large''': 1_0_2_4,
    '''facebook/bart-large-mnli''': 1_0_2_4,
    '''facebook/bart-large-cnn''': 1_0_2_4,
    '''facebook/bart-large-xsum''': 1_0_2_4,
    '''yjernite/bart_eli5''': 1_0_2_4,
}

class 							a__( snake_case__     ):
			a_	: Dict									=      VOCAB_FILES_NAMES
			a_	: Tuple									=      PRETRAINED_VOCAB_FILES_MAP
			a_	: Tuple									=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			a_	: List[Any]									=      ['''input_ids''', '''attention_mask''']
			a_	: Union[str, Any]									=      BartTokenizer






			def __init__(     self					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None					,   _UpperCAmelCase="replace"					,   _UpperCAmelCase="<s>"					,   _UpperCAmelCase="</s>"					,   _UpperCAmelCase="</s>"					,   _UpperCAmelCase="<s>"					,   _UpperCAmelCase="<unk>"					,   _UpperCAmelCase="<pad>"					,   _UpperCAmelCase="<mask>"					,   _UpperCAmelCase=False					,   _UpperCAmelCase=True					,   **_UpperCAmelCase					,   )							->							int:
					super().__init__(
					    _UpperCAmelCase					,   _UpperCAmelCase					,   tokenizer_file=_UpperCAmelCase					,   errors=_UpperCAmelCase					,   bos_token=_UpperCAmelCase					,   eos_token=_UpperCAmelCase					,   sep_token=_UpperCAmelCase					,   cls_token=_UpperCAmelCase					,   unk_token=_UpperCAmelCase					,   pad_token=_UpperCAmelCase					,   mask_token=_UpperCAmelCase					,   add_prefix_space=_UpperCAmelCase					,   trim_offsets=_UpperCAmelCase					,   **_UpperCAmelCase					,   )

					snake_case__	     =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
					if pre_tok_state.get('add_prefix_space'					,   _UpperCAmelCase						) != add_prefix_space:
							snake_case__	     =getattr(_UpperCAmelCase					,   pre_tok_state.pop('type'						)						)
							snake_case__	     =add_prefix_space
							snake_case__	     =pre_tok_class(**_UpperCAmelCase						)

					snake_case__	     =add_prefix_space

					# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
					snake_case__	     ='post_processor'
					snake_case__	     =getattr(self.backend_tokenizer					,   _UpperCAmelCase					,   _UpperCAmelCase						)
					if tokenizer_component_instance:
							snake_case__	     =json.loads(tokenizer_component_instance.__getstate__()						)

							# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
							if "sep" in state:
									snake_case__	     =tuple(state['sep']						)
							if "cls" in state:
									snake_case__	     =tuple(state['cls']						)

							snake_case__	     =False

							if state.get('add_prefix_space'					,   _UpperCAmelCase						) != add_prefix_space:
									snake_case__	     =add_prefix_space
									snake_case__	     =True

							if state.get('trim_offsets'					,   _UpperCAmelCase						) != trim_offsets:
									snake_case__	     =trim_offsets
									snake_case__	     =True

							if changes_to_apply:
									snake_case__	     =getattr(_UpperCAmelCase					,   state.pop('type'						)						)
									snake_case__	     =component_class(**_UpperCAmelCase						)
									setattr(self.backend_tokenizer					,   _UpperCAmelCase					,   _UpperCAmelCase						)






			@property
			def 						_lowercase  (     self						)							->							str:
					if self._mask_token is None:
							if self.verbose:
									logger.error('Using mask_token, but it is not set yet.'						)
							return None
					return str(self._mask_token						)






			@mask_token.setter
			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							int:
					snake_case__	     =AddedToken(_UpperCAmelCase					,   lstrip=_UpperCAmelCase					,   rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase					,   _UpperCAmelCase						) else value
					snake_case__	     =value






			def 						_lowercase  (     self					,   *_UpperCAmelCase					,   **_UpperCAmelCase						)							->							BatchEncoding:
					snake_case__	     =kwargs.get('is_split_into_words'					,   _UpperCAmelCase						)

					if is_split_into_words and not self.add_prefix_space:
							raise ValueError(
							    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
							    'to use it with pretokenized inputs.'						)

					return super()._batch_encode_plus(*_UpperCAmelCase					,   **_UpperCAmelCase						)






			def 						_lowercase  (     self					,   *_UpperCAmelCase					,   **_UpperCAmelCase						)							->							BatchEncoding:
					snake_case__	     =kwargs.get('is_split_into_words'					,   _UpperCAmelCase						)

					if is_split_into_words and not self.add_prefix_space:
							raise ValueError(
							    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
							    'to use it with pretokenized inputs.'						)

					return super()._encode_plus(*_UpperCAmelCase					,   **_UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							Tuple[str]:
					snake_case__	     =self._tokenizer.model.save(_UpperCAmelCase					,   name=_UpperCAmelCase						)
					return tuple(_UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase=None						)							->							Optional[int]:
					snake_case__	     =[self.bos_token_id] + token_ids_a + [self.eos_token_id]
					if token_ids_a is None:
							return output

					return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							List[int]:
					snake_case__	     =[self.sep_token_id]
					snake_case__	     =[self.cls_token_id]

					if token_ids_a is None:
							return len(cls + token_ids_a + sep						) * [0]
					return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]




